Clustering as an example of optimizing arbitrarily chosen objective functions

This paper is a reflection upon a common practice of solving various types of learning problems by optimizing arbitrarily chosen criteria in the hope that they are well correlated with the criterion actually used for assessment of the results. This issue has been investigated using clustering as an example, hence a unified view of clustering as an optimization problem is first proposed, stemming from the belief that typical design choices in clustering, like the number of clusters or similarity measure can be, and often are suboptimal, also from the point of view of clustering quality measures later used for algorithm comparison and ranking. In order to illustrate our point we propose a generalized clustering framework and provide a proof-of-concept using standard benchmark datasets and two popular clustering methods for comparison

Simulation and Augmentation of Social Networks for Building Deep Learning Models

A limitation of the Graph Convolutional Networks (GCNs) is that it assumes at a particular $l^{th}$ layer of the neural network model only the $l^{th}$ order neighbourhood nodes of a social network are influential. Furthermore, the GCN has been evaluated on citation and knowledge graphs, but not extensively on friendship-based social graphs. The drawback associated with the dependencies between layers and the order of node neighbourhood for the GCN can be more prevalent for friendship-based graphs. The evaluation of the full potential of the GCN on friendship-based social network requires openly available datasets in larger quantities. However, most available social network datasets are not complete. Also, the majority of the available social network datasets do not contain both the features and ground truth labels. In this work, firstly, we provide a guideline on simulating dynamic social networks, with ground truth labels and features, both coupled with the topology. Secondly, we introduce an open-source Python-based simulation library. We argue that the topology of the network is driven by a set of latent variables, termed as the social DNA (sDNA). We consider the sDNA as labels for the nodes. Finally, by evaluating on our simulated datasets, we propose four new variants of the GCN, mainly to overcome the limitation of dependency between the order of node-neighbourhood and a particular layer of the model. We then evaluate the performance of all the models and our results show that on 27 out of the 30 simulated datasets our proposed GCN variants outperform the original model

Dual input stream transformer for eye-tracking line assignment

We introduce a novel Dual Input Stream Transformer (DIST) for the challenging problem of assigning fixation points from eye-tracking data collected during passage reading to the line of text that the reader was actually focused on. This post-processing step is crucial for analysis of the reading data due to the presence of noise in the form of vertical drift. We evaluate DIST against nine classical approaches on a comprehensive suite of nine diverse datasets, and demonstrate DIST's superiority. By combining multiple instances of the DIST model in an ensemble we achieve an average accuracy of 98.5\% across all datasets. Our approach presents a significant step towards addressing the bottleneck of manual line assignment in reading research. Through extensive model analysis and ablation studies, we identify key factors that contribute to DIST's success, including the incorporation of line overlap features and the use of a second input stream. Through evaluation on a set of diverse datasets we demonstrate that DIST is robust to various experimental setups, making it a safe first choice for practitioners in the field.Comment: This work has been submitted to the IEEE Transactions on pattern analysis and machine intelligence for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Code will be published after publicatio

Severe depletion of mitochondrial DNA in spinal muscular atrophy

Spinal muscular atrophy (SMA) is a neuromus- cular disorder in childhood leading to a dramatic loss of muscle strength. Functional investigations with high-reso- lution polarography and enzyme measurements of the res- piratory chain revealed lowered activities in muscle tissue of SMA patients. To gain a better understanding of this low energy supply we analyzed the amount of mitochon- drial DNA (mtDNA) in skeletal muscle of 20 unrelated children with genetically proven SMA and 31 controls. Quantitative Southern blot analysis revealed a severe and homogeneous decrease in the content of muscle mtDNA in relation to nuclear DNA in SMA patients (90.3±7.8%), whereas by immunofluorescence no decrease in the num- ber of mitochondria was detected. In addition, a two- to threefold reduction of the nuclear-encoded complex II (succinate dehydrogenase) activity was detected in SMA muscle tissue. Western blot analysis showed a significant reduction of both mitochondrial- and nuclear-encoded cy- tochrome c oxidase subunits. Our results indicate that mtDNA depletion in SMA is a consequence of severe at- rophy, and has to be differentiated by measurement of complex II from an isolated reduction of mtDNA as found in patients with mitochondriocytopathies and the so- called mtDNA depletion syndrome

Wettability and reactivity of ZrB2 substrates with liquid Al

Wetting characteristics of the Al/ZrB2 system were experimentally determined by the sessile drop method with application of separate heating of the ZrB2 and Al samples and combined with in situ cleaning of Al drop from native oxide film directly in vacuum chamber. The tests were performed in ultrahigh vacuum of 10−6 mbar at temperatures 710, 800, and 900 °C as well as in flowing inert gas (Ar) atmosphere at 1400 °C. The results evidenced that liquid Al does not wet ZrB2 substrate at 710 and 800 °C, forming high contact angles (θ) of 128° and 120°, respectively. At 900 °C, wetting phenomenon (θ < 90°) occurs in 29th minute and the contact angle decreases monotonically to the final value of 80°. At 1400 °C, wetting takes place immediately after drop deposition with a fast decrease in the contact angle to 76°. The solidified Al/ZrB2 couples were studied by scanning and transmission electron microscopy coupled with x-ray energy diffraction spectroscopy. Structural characterization revealed that only in the Al/ZrB2 couple produced at the highest temperature of 1400 °C new phases (Al3Zr, AlB2 and α-Al2O3) were formed

Can we identify non-stationary dynamics of trial-to-trial variability?"

Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings

Tickborne Encephalitis in Naturally Exposed Monkey (Macaca sylvanus)

We describe tickborne encephalitis (TBE) in a monkey (Macaca sylvanus) after natural exposure in an area at risk for TBE. TBE virus was present in the brain and could be identified as closely related to the European subtype, strain Neudoerfl

Water supply of ancient Egyptian settlements: the role of the state. Overview of a relatively equitable scheme from the Old to New Kingdom (ca. 2543-1077 BC).

This is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1007/s12685-015-0150-xThe study of the textual and archaeological evidence shows that the water supply of the settlements of ancient Egypt seems to have worked on a simple and a relatively equitable scheme, at least from the Old Kingdom until the New Kingdom (ca. 2543-1077). The water supply of the inhabitants was completely managed by the state, through the local administration which was charged to bring the water, in general from a rural area, into towns and cities and to redistribute it to the inhabitants. The method of supply is illustrated by several sources of evidence, in particular by the well known case of the "water-carriers" of the village of Deir el-Medina. Thus, drawing together text and archaeology, this paper will demonstrate that over an extended period, even when the city was far from a water source, the state did not set up complex installations such as pipe networks or wells to bring water, but preferred a simpler system using the manpower available

IFNβ Protects Neurons from Damage in a Murine Model of HIV-1 Associated Brain Injury.

Infection with human immunodeficiency virus-1 (HIV-1) causes brain injury. Type I interferons (IFNα/β) are critical mediators of any anti-viral immune response and IFNβ has been implicated in the temporary control of lentiviral infection in the brain. Here we show that transgenic mice expressing HIV-1 envelope glycoprotein 120 in their central nervous system (HIVgp120tg) mount a transient IFNβ response and provide evidence that IFNβ confers neuronal protection against HIVgp120 toxicity. In cerebrocortical cell cultures, neuroprotection by IFNβ against gp120 toxicity is dependent on IFNα receptor 1 (IFNAR1) and the β-chemokine CCL4, as IFNAR1 deficiency and neutralizing antibodies against CCL4, respectively, abolish the neuroprotective effects. We find in vivo that IFNβ mRNA is significantly increased in HIVgp120tg brains at 1.5, but not 3 or 6 months of age. However, a four-week intranasal IFNβ treatment of HIVgp120tg mice starting at 3.5 months of age increases expression of CCL4 and concomitantly protects neuronal dendrites and pre-synaptic terminals in cortex and hippocampus from gp120-induced damage. Moreover, in vivo and in vitro data suggests astrocytes are a major source of IFNβ-induced CCL4. Altogether, our results suggest exogenous IFNβ as a neuroprotective factor that has potential to ameliorate in vivo HIVgp120-induced brain injury

How STRANGE are your study animals?

A new framework for studies of animal behaviour will help to avoid sampling bias— ten years on from the call to widen the pool of human participants in psychology research beyond the WEIRD.Publisher PDFNon peer reviewe